A Non-Contact Detection Method for Multi-Person Vital Signs Based on IR-UWB Radar

With the vigorous development of ubiquitous sensing technology, an increasing number of scholars pay attention to non-contact vital signs (e.g., Respiration Rate (RR) and Heart Rate (HR)) detection for physical health. Since Impulse Radio Ultra-Wide Band (IR-UWB) technology has good characteristics,...

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Vydáno v:Sensors (Basel, Switzerland) Ročník 22; číslo 16; s. 6116
Hlavní autoři: Dang, Xiaochao, Zhang, Jinlong, Hao, Zhanjun
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.08.2022
MDPI
Témata:
Algorithms
Antennas
CIR
Coronaviruses
COVID-19
crossing threshold
Earthquakes
Epidemics
Heart beat
Heart rate
Human body
IR-UWB radar non-contact
Methods
Personal protective equipment
Principal components analysis
Propagation
Radar
Radar systems
Receivers & amplifiers
Respiration
smoothing splines
Spectrum allocation
Vital signs
ISSN:1424-8220, 1424-8220
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Shrnutí:With the vigorous development of ubiquitous sensing technology, an increasing number of scholars pay attention to non-contact vital signs (e.g., Respiration Rate (RR) and Heart Rate (HR)) detection for physical health. Since Impulse Radio Ultra-Wide Band (IR-UWB) technology has good characteristics, such as non-invasive, high penetration, accurate ranging, low power, and low cost, it makes the technology more suitable for non-contact vital signs detection. Therefore, a non-contact multi-human vital signs detection method based on IR-UWB radar is proposed in this paper. By using this technique, the realm of multi-target detection is opened up to even more targets for subjects than the more conventional single target. We used an optimized algorithm CIR-SS based on the channel impulse response (CIR) smoothing spline method to solve the problem that existing algorithms cannot effectively separate and extract respiratory and heartbeat signals. Also in our study, the effectiveness of the algorithm was analyzed using the Bland–Altman consistency analysis statistical method with the algorithm’s respiratory and heart rate estimation errors of 5.14% and 4.87%, respectively, indicating a high accuracy and precision. The experimental results showed that our proposed method provides a highly accurate, easy-to-implement, and highly robust solution in the field of non-contact multi-person vital signs detection.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s22166116